Devices, software and methods for measuring packet loss burstiness to determine quality of voice data transmission through a network

ABSTRACT

Devices, software, and methods measure a burstiness of packet loss episodes in transmissions of voice data through networks. At least one burstiness statistic is determined to quantify how the lost packets are distributed with respect to the received packets within the sequence. The burstiness statistic is optionally used to determine a figure of merit, which in turn can be used to give a grade for predicting how well a packet loss concealment scheme will work.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.09/735,429, filed Dec. 12, 2000, now U.S. Pat. No. 6,930,982.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention is related to the field of voice data transmissionthrough a network, and more specifically to devices, software, andmethods for measuring a burstiness of packet loss in determining aquality of voice for network telephony.

2. Description of the Related Art

Voice transmissions, such as telephone calls, are increasingly made overpacket switched networks. Unlike traditional telephone lines, networksbreak down the voice into data, arrange the data in distinct packets,and send the packets across the network. The voice is reconstructed atthe other end of the voice call for the listener.

Sometimes packets can be lost. This can happen when packets are droppedfrom the network, or are delivered too late to be incorporated in thereconstructed voice sequence. In that case, the packets are absent, butare generally called lost packets in the art.

When a packet is lost, that means that a portion of the voice messagedoes not reach the listener. In its place, the listener hears a smallinterruption, or the voice is syncopated. Thus, losing packets degradesthe quality of the voice call, and thus of the telephone service.

There have been efforts to rectify at least the problem of low callquality. For example, when a single packet is lost, its data can bereconstructed from its immediately previous data. So, even though itsinformation may be actually lost, the user will not hear aninterruption. This is called packet loss concealment.

Packet loss concealment is harder to perform, however, when many packetsare lost in a large group. They are harder to reconstruct, because formany the immediately previous data is not available.

In addition, there have been efforts to monitor the quality of service,so that other corrective measures can be taken. These efforts includequantifying the quality of service, in other words, determining how poorthe service is at any given time. Once service is determined to be poor,other corrective measures can be taken.

Quality of service is quantified by measuring other parameters inaddition to packet loss. These other parameters are delay, echo, codecdegradation, etc. All the parameters are taken together to determine atotal voice quality statistic, or figure of merit.

Packet loss is traditionally measured in the prior art as a rate Raccording to Equation (1):R=(number of lost packets)/(total number of packets)  Equation (1)

Equation (1) is used to determine voice quality. By using Equation (1),however, the prior art makes a fundamental assumption about the natureof packet loss. The assumption is that packet loss is distributeduniformly over the duration telephone call.

The assumption of the prior art does not take into account the truenature of packet loss. Packet loss is not distributed uniformly over theduration telephone call. Instead, it tends to come in bursts, or groups.Worse, some bursts last longer than others. Yet the present systems donot detect that, which is a deficiency.

The deficiency of the prior art is illustrated with reference to FIG. 1.Three waveforms A, B, C, are given for three sample voice datatransmissions, all of the same time duration. The waveforms A, B, Cillustrate packets PR as they are received, prior to any datareconstruction for packet loss concealment.

Each of the three waveforms A, B, C, reflects a sequence asreconstructed. Each sequence leaves blanks PL for absent packets PL.Packets are absent either because they are lost, or simply arrive toolate to be incorporated in the play out.

In all three waveforms A, B, C, one fifth, or 20% of the packets are notreceived. These are indicated as lost packets PL. So, for all threewaveforms A, B, C, Equation (1) returns R=20%. This yields the samevoice call quality grade for all three. In the example of FIG. 1, thatgrade is “FAIR”.

It will be appreciated that the lost packets PL are grouped differentlyin each of waveforms A, B, C. In waveform A, the lost packets PL aredistributed uniformly over the examined duration of the voice call. Moreparticularly, every fifth packet PL is missing. In waveform B, the lostpackets PL occur in somewhat bigger bursts. As drawn, every ninth andtenth packet PL are missing. In waveform C, all 10 packets are lost inone big burst.

In reality, for waveform B, the perception of the voice call qualitywill be somewhat worse than in waveform A, while the perception forwaveform C will be by far the worst. That is because it will be harderto reconstruct the missing packets, since they occur in bursts. As such,waveform B deserves a worse grade than waveform A. For the same reason,waveform C deserves an even worse grade. That is because packet lossconcealment is the hardest to perform for waveform C. Many of theimmediately previous data is not available, and the voice will beperceived as lost.

The problem, however, is that the differentiation in eventual quality ofthe reconstruction is not predicted by the grading system.

BRIEF SUMMARY OF THE INVENTION

The present invention overcomes these problems and limitations of theprior art.

Generally, the present invention provides devices, software, and methodsfor measuring a burstiness of packet loss episodes, in determining aquality of a voice transmission through a network. More specifically,the invention teaches to determine a burstiness statistic, forquantifying how the lost packets are distributed with respect to thereceived packets within the sequence. The burstiness statistic isoptionally used to determine a figure of merit, which in turn can beused to give a grade for predicting how well a packet loss concealmentscheme will work.

The invention finds applications in broadcasting through a network (suchas internet radio) and network telephony.

The invention will become more readily apparent from the followingDetailed Description, which proceeds with reference to the drawings, inwhich:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates three possible waveforms of received packets for avoice data transmission, and quality grades assigned to these waveformsby systems of the prior art.

FIG. 2 is a block diagram of a transmitting device made according to anembodiment of the present invention.

FIG. 3 is a block diagram of a receiving device made according to anembodiment of the present invention.

FIG. 4 is a flowchart illustrating methods according to embodiments ofthe present invention.

FIG. 5 is a flowchart illustrating methods for performing a box of theflowchart of FIG. 4.

FIG. 6 illustrates the three possible waveforms of FIG. 1, and voicetransmission quality grades assigned to these waveforms by embodimentsof FIG. 5.

FIG. 7 is a flowchart illustrating methods for performing a box of theflowchart of FIG. 4.

FIG. 8 illustrates the three possible waveforms of FIG. 1, and voicetransmission quality grades assigned to these waveforms by embodimentsof FIG. 7.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

As has been mentioned, the present invention provides devices, software,and methods for measuring an additional statistic for quality of voicein Voice over Internet Protocol (VoIP) transmissions. The additionalstatistic measures a burstiness, grouping, or clustering in the patternof lost packets. The invention is now described in more detail.

Referring to FIG. 2, a transmitting device 200 made according to anembodiment of the invention is described. Device 200 is for transmittingvoice packets through a network 120. Device 200 preferably transmits thevoice packets to a network switch 140, such as Gateway-A 140, of thenetwork 120.

One or more of the components of device 200 can be implemented incombination with each other, consistently with components of thisdescription. For example, device 200 can be implemented as part of alarger Digital Signal Processing (DSP) architecture.

In the embodiment of FIG. 2, device 200 includes an encoder 210 forencoding a frame of voice data into a voice packet. Encoder 210 includesa processor 212, which is also referred to as Central Processing Unit(CPU) 212, and a memory 214. The processor 212 is adapted to perform themethod of the invention. Preferably it is so adapted by running aprogram 216 made according to the invention, which resides on memory214.

In addition, device 200 optionally includes a transmit buffer 230adapted to receive inputs from the encoder 210, for storing the encodedframes prior to transmitting.

Device 200 includes a network interface (not shown separately) forinterfacing with network 120. The network interface can be implementedas a stand-alone feature, or in conjunction with another component, suchas transmit buffer 230.

Referring to FIG. 3, a receiving device 300 according to the inventionis described. One or more of the components of device 300 can beimplemented in combination with each other, consistently with componentsof this description. For example, device 300 can be implemented as partof a larger Digital Signal Processing (DSP) architecture.

In the embodiment of FIG. 3, device 300 includes a jitter buffer 310.This stores a number of frames as they are received from the network120. The jitter buffer thus prevents an anomaly that could beexperienced, if frames were played out exactly when they are received.Due to the nature of transmission through the network 120, they can bereceived in bunches, with gaps between the bunches. Jitter buffer 310permits playing them out at a regular pace, notwithstanding when exactlythey are received.

Device 300 also includes a decoder 370. Decoder 370 includes a processor372, which is also referred to as Central Processing Unit (CPU) 372, anda memory 374. The processor 372 is adapted to perform the method of theinvention. Preferably it is so adapted by running a program 376 madeaccording to the invention, which resides on memory 374.

Device 300 can also include other components, such as a Digital toAnalog Converter (DAC) 380. This converts the decoded voice data into ananalog signal, which can be input in a speaker 390.

Device 300 includes a network interface (not shown separately) forinterfacing with network 120. The network interface can be implementedas a stand-alone feature, or in conjunction with another component, suchas jitter buffer 310. Device 300 is deemed to interface with a networkswitch 160, such as Gateway-B 160, of network 120.

As an example, a device that may incorporate aspects of the presentinvention would be an Internet Protocol (IP) telephone. Its transmittingside could include device 200 of FIG. 2, while its receiving side couldinclude device 300 of FIG. 3.

A device of the invention need not be limited to two-way voicecommunication. Devices, software and methods for one way transmitting,such as broadcasting, are also included.

The invention can be performed at the conclusion of a transmission, forexample to calibrate a system. That would be using all the packets ofthe voice transmission, which can be 10,000 for a telephone call.Alternately the invention can be performed dynamically during the voicetransmission by either participant. In that case, it can look at asegment of a waveform or sequence.

It is readily apparent that the present invention can be implemented byone or more devices that include logic circuitry. It can also beimplemented by a device that includes a dedicated processor system, thatcan include a microcontroller or a microprocessor.

The invention additionally provides methods, which are described below.Moreover, the invention provides apparatus that performs, or assists inperforming the methods of the invention. This apparatus may be speciallyconstructed for the required purposes, or it may comprise ageneral-purpose computer selectively activated or reconfigured by acomputer program stored in the computer. The methods and algorithmspresented herein are not necessarily inherently related to anyparticular computer or other apparatus. In particular, variousgeneral-purpose machines may be used with programs in accordance withthe teachings herein, or it may prove more convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these machines will appear from thisdescription.

Useful machines or articles for performing the operations of the presentinvention include general-purpose digital computers or other similardevices. In all cases, there should be borne in mind the distinctionbetween the method of operating a computer and the method of computationitself. The present invention relates also to method steps for operatinga computer and for processing electrical or other physical signals togenerate other desired physical signals.

The invention additionally provides a program, and a method of operationof the program. The program is most advantageously implemented as aprogram for a computing machine, such as a general purpose computer, aspecial purpose computer, a microprocessor, etc.

The invention also provides a storage medium that has the program of theinvention stored thereon. The storage medium is a computer-readablemedium, such as a memory, and is read by the computing machine mentionedabove.

A program is generally defined as a sequence of steps leading to adesired result. These steps, also known as instructions, are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical ormagnetic signals capable of being stored, transferred, combined,compared, and otherwise manipulated or processed. When stored, they canbe stored in any computer-readable medium. It is convenient at times,principally for reasons of common usage, to refer to these signals asbits, data bits, samples, values, elements, symbols, characters, images,terms, numbers, or the like. It should be borne in mind, however, thatall of these and similar terms are associated with the appropriatephysical quantities, and that these terms are merely convenient labelsapplied to these physical quantities.

This detailed description is presented largely in terms of flowcharts,display images, algorithms, and symbolic representations of operationsof data bits within a computer readable medium, such as a memory. Suchdescriptions and representations are the type of convenient labels usedby those skilled in programming and/or the data processing arts toeffectively convey the substance of their work to others skilled in theart. A person skilled in the art of programming can use this descriptionto readily generate specific instructions for implementing a programaccording to the present invention. For the sake of economy, however,flowcharts used to describe methods of the invention are not repeated inthis document for describing software according to the invention.

Often, for the sake of convenience only, it is preferred to implementand describe a program as various interconnected distinct softwaremodules or features, collectively also known as software. This is notnecessary, however, and there may be cases where modules areequivalently aggregated into a single program with unclear boundaries.In any event, the software modules or features of the present inventioncan be implemented by themselves, or in combination with others. Eventhough it is said that the program can be stored in a computer-readablemedium, it should be clear to a person skilled in the art that it neednot be a single memory, or even a single machine. Various portions,modules or features of it can reside in separate memories, or evenseparate machines. The separate machines may be connected directly, orthrough a network, such as a local access network (LAN), or a globalnetwork, such as the Internet.

In the present case, methods of the invention are implemented by machineoperations. In other words, embodiments of the program of the inventionare made such that they perform methods of the invention that aredescribed in this document. These can be optionally performed inconjunction with one or more human operators performing some, but notall of them. As per the above, the users need not be collocated witheach other, but each only with a machine that houses a portion of theprogram. Alternately, some of these machines can operate automatically,without users and/or independently from each other.

Methods of the invention are now described.

Referring now to FIG. 4, a flowchart 400 is used to illustrate methodsaccording to embodiments of the invention.

According to a box 410, packets are received that contain voice data.Voice data are those data that represent sound, such as voice. Thepackets are received from a network, such as network 120 of FIG. 3.

According to a next box 420, an intended sequence of the voice data isdetermined. This is preferably performed by examining sequence data inthe received packets.

According to a next box 430, the received packets are arranged in thesequence. This may leave blanks in the sequence.

According to a next box 440, lost packets PL are inferred to be in theblanks of the sequence.

According to a next box 450, a burstiness statistic is computed, forquantifying how the lost packets are distributed with respect to thereceived packets within the sequence. It will be perfectly apparent to aperson skilled in the art that a burstiness statistic according to theinvention is equivalently defined if it quantifies how the receivedpackets are distributed with respect to the lost packets within thesequence.

A person skilled in the art will be able to determine a large number ofburstiness statistics that can be computed according to the invention.By way of example, and not of limitation, two methods according to theinvention are described later in this document, each of which yields atleast two possible burstiness statistics.

According to an optional next box 460, a figure of merit is determinedfor the sequence from any one or more of the burstiness statisticscomputed according to the invention. The figure of merit can be anequipment impairment factor IE, which in turn can use other parameters.

The figure of merit is used to determine the overall grade given to thesequence. Optionally and preferably, it is also computed from theaverage packet loss rate R. This can be performed either by a tablelookup, or using a polynomial function. The table or the coefficients ofthe polynomial can be determined using subjective or objective speechquality test results.

Referring now to FIG. 5, a flowchart 500 is used for illustratingperforming box 450 of FIG. 4 according to an embodiment of theinvention.

According to a box 510, a duration is counted in the sequence of apacket loss episode. This is in the form of a number of lost packets PL,which is also known as duration number DPL. A packet loss episode is anepisode of contiguously occurring lost packets PL. Equivalently, aduration number can be a number of contiguously occurring receivedpackets PR. It is preferred that more than one duration numbers of thesame type are counted.

According to an optional next box 520, the longest packet loss episodeis determined. This is preferably performed by determining a maximum, orlargest one, of the duration numbers DPL.

According to an optional next box 530, the packet loss episodes (NPL)are counted. Equivalently, the complementary packet received instancescan be counted. While the complementary number may differ by one fromNPL, the large numbers involved will make the difference insubstantial.

According to an optional next box 540, a number of packets is counted.In the preferred embodiment, the packets that are counted are the lostpackets PL, yielding a variable that is denoted by B.

According to an optional next box 550, the average packet loss duration(ADU) is computed. Computation is performed preferably according toEquation (2):ADU=B/NPL  Equation (2)

According to an optional next box 560, a variance of ADU is computed.

Referring to FIG. 6, the result of the embodiments of the invention canbe appreciated in full. FIG. 6 shows the three waveforms of FIG. 1,except grading takes place according to the methods of FIG. 4 and FIG.5, not the prior art.

In waveform A, the lost packets PL are the least grouped, and thus theburstiness is the least. Each gap is a packet loss episode PL. Equation(2) yields ADU=1 as a measure of the amount of burstiness. The grade“GOOD” can thus be assigned, since it can be anticipated that packetloss concealment can accommodate completely the little burstiness. Thisis even though the average packet loss rate P is 20%.

In waveform B, the lost packets PL are grouped or clustered a littlemore, and thus there is somewhat higher burstiness. Equation (2) yieldsADU=2 as a measure of the amount of burstiness. Indeed, duration numberDP would be DPLB=2 for all gaps of lost packets PL. The grade “FAIR” canthus be assigned, since it can be anticipated that packet lossconcealment can accommodate mostly, but not completely, this amount ofburstiness.

In waveform C, the lost packets PL are the most grouped, and thus theburstiness is the highest. Equation (2) yields ADU=10 as a measure ofthe amount of burstiness. Indeed, duration number DP would be DPLC=10for the single gap. The grade “POOR” can thus be assigned, since thesenumbers, combined with an average packet loss rate of R=20%, can be usedto anticipate that the known packet loss concealment techniques will notsucceed in the face of such large gaps.

It will be appreciated that the grades of FIG. 6 are more discerningthan those of FIG. 1, and work better to predict the success of a packetloss concealment scheme.

Referring now to FIG. 7, a flowchart 700 is used for illustratingperforming box 450 of FIG. 4 according to another embodiment of theinvention. In the embodiment of FIG. 7, the burstiness statistic iscomputed by counting transitions between received packets PR and lostpackets PL, using a Markov chain type model for the sequence. For agiven average loss rate R, the more the transitions, the smaller theduration of each packet loss episode is inferred to be. The less eachpacket loss episode lasts, the less the burstiness, and a higher gradecan be assigned.

More specifically, according to a box 710, good states are defined inthe sequence where there are at least some received packets PR. Inaddition, bad states are defined in the sequence where there are lostpackets PL.

According to a next box 720, the transitions are counted between thegood states in the bad states. In the preferred embodiment, thetransitions that are counted are from the bad states to the good states.That variable is denoted by U.

According to a next box 730, a number of packets is counted. In thepreferred embodiment, the packets that are counted are the lost packets,yielding a variable that is denoted by B.

According to next box 740, the counted number of transitions is dividedby the counted number of packets. This yields a variable that is denotedby Q, according to Equation (3):Q=U/B  Equation (3)

A number of different embodiments are now described with reference toboxes 750 and 760. As will be appreciated, execution can proceed frombox 740, to box 750, to box 760, to box 770. Alternately, the order ofbox 750 and box 760 could be reversed. In addition, one of box 750 andbox 760 can be omitted.

Referring now to box 750, the total packets T are counted. In addition,the average loss ratio R is computed according to equation (1), which isrepeated below:R=(number of lost packets)/(total number of packets)=B/T  Equation (1)

Referring now to box 760, the complementary transitions are counted,i.e. those from the good states to the bad states. This yields avariable D. In addition, the received packets PR are counted, whichyields a variable G. Moreover, a variable P is computed from Equation(4) below. It will be appreciated that variable P is complementary tovariable Q.P=D/G  Equation (4)

According to an optional next box 770, a normalized burstiness statisticQn is computed according to Equation (5) below:Qn=Q/(1−P)  Equation (5)

It should also be appreciated that the normalized burstiness statisticof the equation (5) is chosen so as to amplify the computation ofvariable Q.

Now it will be appreciated why one of box 750, and 760 can be omitted.The above equations can be solved in the way as to avoid at least one ofthese boxes. For example, a corollary of these equations is equation (6)below:R=P/(P+Q)  Equation (6)

Referring to FIG. 8, the result of the embodiments of the invention canbe appreciated in full. FIG. 8 shows the three waveforms of FIG. 1,except grading takes place according to the methods of FIG. 4 and FIG.7, not the prior art. The waveforms show the transitions between thegood and the bad states (i.e. from PL to PR, and vice versa) as arrows.Transitions at the beginning and the end of the waveform are notincluded, as they can be arbitrary.

In waveform A, the lost packets PL are the least grouped. Equations (3)and (5) yield Q=1, and Qn=1.33. The grade “GOOD” can thus be assigned,for the same rationale as in FIG. 6.

In waveform B, the lost packets PL are clustered a little more.Equations (3) and (5) yield Q=0.5, and Qn=0.57. The grade “FAIR” canthus be assigned, for the same rationale as in FIG. 6.

In waveform C, the lost packets PL are the most grouped. Equations (3)and (5) yield Q=0.125, and Qn=0.125. The grade “POOR” can thus beassigned, for the same rationale as in FIG. 6.

Again, it will be appreciated that the grades of FIG. 8 are morediscerning than those of FIG. 1, and work better to predict the successof a packet loss concealment scheme.

The above description for a Markov chain type model can be extended forthe implementations of FIG. 6. In addition, more burstiness parameterscan be computed with a more general Markov chain type model. Forexample, one may use an M-state Markov chain model, having two states.The first state−1 is “good”, state m corresponds a packet loss episodeof length M−1, and so on. One will recognize that the examples workedabove where for M=2, but that is not limiting. In fact, extending to ahigher M yields another M−1 burstiness statistics that may be used asper the above.

A person skilled in the art will be able to practice the presentinvention in view of the description present in this document, which isto be taken as a whole. Numerous details have been set forth in order toprovide a more thorough understanding of the invention. In otherinstances, well-known features have not been described in detail inorder not to obscure unnecessarily the invention.

While the invention has been disclosed in its preferred form, thespecific embodiments thereof as disclosed and illustrated herein are notto be considered in a limiting sense. Indeed, it should be readilyapparent to those skilled in the art in view of the present descriptionthat the invention can be modified in numerous ways. The inventorregards the subject matter of the invention to include all combinationsand subcombinations of the various elements, features, functions and/orproperties disclosed herein.

The following claims define certain combinations and subcombinations,which are regarded as novel and non-obvious. Additional claims for othercombinations and subcombinations of features, functions, elements and/orproperties may be presented in this or a related document.

1. In a system that receives packets from a network, said packetscontaining data that represents sound, a method comprising: determiningan intended sequence of said packets; determining the actual sequence ofsaid packets, there being empty positions in said actual sequence whereappropriate packets are not present; determining the distribution ofsaid empty positions in said actual sequence; and calculating by anapparatus, a burstiness statistic that quantifies said distribution,said burstiness statistic not being directly dependent on packet lossrate.
 2. The method of claim 1 wherein determining the distribution ofempty positions in said actual sequence quantifies how lost packets aredistributed with respect to received packets.
 3. The method of claim 1wherein determining the distribution of empty positions in said actualsequence quantifies how received packets are distributed with respect tolost packets.
 4. The method of claim 1 including determining a figure ofmerit for said actual sequence from said burstiness statistic.
 5. Themethod recited in claim 1 including calculating the packet loss rate. 6.The method of claim 1 including determining a figure of merit for saidactual sequence from said burstiness statistic and from a calculation ofthe packet loss rate.
 7. The method of claim 6 wherein said burstinessstatistic includes an indication of the length of said packet lossepisodes.
 8. The method of claim 7 wherein said method includes countingthe number of transitions from good states to bad states.
 9. The methodof claim 7 wherein said method includes counting the number oftransitions from bad states to good states.
 10. The method of claim 1wherein a packet loss episode includes a series of packets that includesempty positions and wherein the number of packet loss episodes iscounted.
 11. The method of claim 1 wherein good states are defined wheresaid intended sequence and said actual sequence of packets are the sameto some extent, and bad states are defined where there are at least someempty positions in said actual sequence.
 12. The method recited in claim1 wherein the calculation of said burstiness statistic includes:measuring the number of occurrences a first pre-defined sub-sequence ofappropriate packets; measuring the number of occurrences of a secondpre-defined sub-sequence of empty positions; and counting the number oftransitions in said actual sequence between said first and secondpredefined sub-sequences.
 13. A method comprising: receiving, from anetwork, packets that encode data which represent sound; determining anintended sequence of the sound data from the received packets; arrangingthe received packets in the sequence; inferring absent packets in placesin the sequence in which the corresponding packets were lost or dropped;measuring durations for the absent packet episodes; and determining byan apparatus, a burstiness statistic for quantifying an absent packetdistribution for one or more segments of the sequence according to themeasured durations for the contiguous absent packet episodes, whereinthe burstiness statistic is derived independently of a packet loss rate;and determining a figure of merit for one or more segments of thesequence from one or more parameters, one parameter of which is theburstiness statistic.
 14. The method of claim 13, wherein the burstinessstatistic is determined for one or more segments of the sequence, wherea segment is defined by one of the following: contiguous absent andreceived packets that do not include some defined number of contiguousreceived packets; contiguous absent and received packets that includesome defined number of contiguous absent packets that are immediatelyfollowed and immediately preceded by some defined number of contiguousreceived packets; contiguous absent and received packets that includesome defined number of contiguous absent packets that are immediatelypreceded by contiguous received packets at the beginning of the sequenceand are immediately followed by some defined number of contiguousreceived packets; and contiguous absent and received packets that areimmediately followed by contiguous received packets at the end of thesequence and are immediately preceded by some defined number ofcontiguous received packets.
 15. The method of claim 14, wherein theburstiness statistic is determined for one or more of the definedsegments of the sequence according to the average packet loss duration.16. The method of claim 14, wherein the burstiness statistic isdetermined for one or more of the defined segments of the sequence by:defining good states in one or more segments that correspond to at leastsome of the received packets; defining bad states in one or moresegments that correspond to at least some of the absent packets; andcounting the number of transitions between the good states and the badstates.
 17. The method of claim 16, wherein the burstiness statistic isdetermined for one or more of the defined segments of the sequence by:counting the number of packets in one or more segments and dividing thecounted number of transitions by the counted number of packets.
 18. In asystem that receives packets from a network, said packets containingdata that represents sound, the combination of: means for receivingpackets from a network means for determining an intended sequence ofsaid packets, means for determining the actual sequence of said packets,there being empty positions in said actual sequence where appropriatepackets are not present; means for determining the distribution of saidempty positions in said actual sequence, and means for calculating aburstiness statistic that quantifies said distribution, said burstinessstatistic not being directly dependent on packet loss rate.
 19. Thecombination of claim 18 wherein said means for determining thedistribution of empty positions in said actual sequence quantifies howlost packets are distributed with respect to received packets.
 20. Thecombination of claim 18 wherein said means for determining thedistribution of empty positions in said actual sequence quantifies howreceived packets are distributed with respect to lost packets.
 21. Thecombination of claim 18 including means for determining a figure ofmerit for said actual sequence from said burstiness statistic.
 22. Anarticle of manufacture comprising: a computer usable medium havingcomputer readable program code means embodied therein for causing acomputer processor to execute the method recited in claim
 1. 23. Acomputer readable medium containing instructions which, when executed ina processing system, cause the processing system to perform the methodrecited in claim 2.